B-CNN: Branch Convolutional Neural Network for Hierarchical Classification

Abstract

Convolutional Neural Network (CNN) image classifiers are traditionally designed to have sequential convolutional layers with a single output layer. This is based on the assumption that all target classes should be treated equally and exclusively. However, some classes can be more difficult to distinguish than others, and classes may be organized in a hierarchy of categories. At the same time, a CNN is designed to learn internal representations that abstract from the input data based on its hierarchical layered structure. So it is natural to ask if an inverse of this idea can be applied to learn a model that can predict over a classification hierarchy using multiple output layers in decreasing order of class abstraction. In this paper, we introduce a variant of the traditional CNN model named the Branch Convolutional Neural Network (B-CNN). A B-CNN model outputs multiple predictions ordered from coarse to fine along the concatenated convolutional layers corresponding to the hierarchical structure of the target classes, which can be regarded as a form of prior knowledge on the output. To learn with B-CNNs a novel training strategy, named the Branch Training strategy (BT-strategy), is introduced which balances the strictness of the prior with the freedom to adjust parameters on the output layers to minimize the loss. In this way we show that CNN based models can be forced to learn successively coarse to fine concepts in the internal layers at the output stage, and that hierarchical prior knowledge can be adopted to boost CNN models’ classification performance. Our models are evaluated to show that the B-CNN extensions improve over the corresponding baseline CNN on the benchmark datasets MNIST, CIFAR-10 and CIFAR-100.

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Citation

@article{DBLP:journals/corr/abs-1709-09890,
  author    = {Xinqi Zhu and Michael Bain},
  title     = {B-CNN: Branch Convolutional Neural Network for Hierarchical Classification},
  journal   = {CoRR},
  volume    = {abs/1709.09890},
  year      = {2017},
  url       = {http://arxiv.org/abs/1709.09890},
  archivePrefix = {arXiv},
  eprint    = {1709.09890},
  timestamp = {Mon, 13 Aug 2018 16:48:54 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1709-09890.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}